Building an Archive with Saada

Saada transforms a set of heterogeneous FITS files or VOTables of various categories (images, tables, spectra ...) in a database without writing code. Databases created with Saada come with a rich Web interface and an Application Programming Interface (API). They support the four most common VO services. Such databases can mix various categories of data in multiple collections. They allow a direct access to the original data while providing a homogenous view thanks to an internal data model compatible with the characterization axis defined by the VO. The data collections can be bound to each other with persistent links making relevant browsing paths and allowing data-mining oriented queries.Comment: 18 pages, 5 figures Special VO issu

Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra

Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad hypothesis behind our work is that letting the abundant real astrophysical data speak for itself, with minimal supervision and no labels, can reveal interesting patterns that may facilitate discovery of novel physical relationships. Here, as the first step, we seek to interpret the representations a deep convolutional neural network chooses to learn, and find correlations in them with current physical understanding. We train an encoder–decoder architecture on the self-supervised auxiliary task of reconstruction to allow it to learn general representations without bias towards any specific task. By exerting weak disentanglement at the information bottleneck of the network, we implicitly enforce interpretability in the learned features. We develop two independent statistical and information-theoretical methods for finding the number of learned informative features, as well as measuring their true correlation with astrophysical validation labels. As a case study, we apply this method to a data set of ∼270 000 stellar spectra, each of which comprising ∼300 000 dimensions. We find that the network clearly assigns specific nodes to estimate (notions of) parameters such as radial velocity and effective temperature without being asked to do so, all in a completely physics-agnostic process. This supports the first part of our hypothesis. Moreover, we find with high confidence that there are ∼4 more independently informative dimensions that do not show a direct correlation with our validation parameters, presenting potential room for future studies

The X-ray source content of the XMM-Newton Galactic plane survey

We report the results of an optical campaign carried out by the XMM-Newton Survey Science Centre with the specific goal of identifying the brightest X-ray sources in the XMM-Newton Galactic plane survey. In addition to photometric and spectroscopic observations obtained at the ESO-VLT and ESO-3.6 m, we used cross-correlations with the 2XMMi, USNO-B1.0, MASS, and GLIMPSE catalogues to advance the identification process. Active coronae account for 16 of the 30 positively or tentatively identified X-ray sources and exhibit the softest X-ray spectra. Many of the identified hard X-ray sources are associated with massive stars, possible members of binary systems and emitting at intermediate X-ray luminosities of 1032−34 erg s−1. Among these are (i) a very absorbed, likely hyper-luminous star with X-ray/optical spectra and luminosities comparable to those of η Carina; (ii) a new X-rayselected WN8 Wolf-Rayet star in which most of the X-ray emission probably arises from wind collision in a binary; (iii) a new Be/X-ray star belonging to the growing class of γ-Cas analogues; and (iv) a possible supergiant X-ray binary of the kind discovered recently by INTEGRAL. One of the sources, XGPS-25, has a counterpart of moderate optical luminosity that exhibits HeII λ4686 and Bowen CIII-NIII emission lines, suggesting that this may be a quiescent or X-ray shielded low mass X-ray binary, although its X-ray properties might also be consistent with a rare kind of cataclysmic variable (CV). We also report the discovery of three new CVs, one of which is a likely magnetic system displaying strong X-ray variability. The soft (0.4–2.0 keV) band log N(>S )−log S curve is completely dominated by active stars in the flux range of 1 × 10−13 to 1 × 10−14 erg cm−2 s−1. Several active coronae are also detected above 2 keV suggesting that the population of RS CVn binaries contributes significantly to the hard X-ray source population. In total, we are able to identify a large fraction of the hard (2–10 keV) X-ray sources in the flux range of 1 × 10−12 to 1 × 10−13 erg cm−2 s−1 with Galactic objects at a rate consistent with what is expected for the Galactic contribution alone.We thank an anonymous referee for useful comments which helped to improve the quality of this paper. We are grateful to O. Herent for carrying out some of the observations presented in this work. This work has been supported in part by the DLR (Deutsches Zentrum für Luftund Raumfahrt) under grants 50 OX 0201 and 50 OX 0801. I.N. is supported by the Spanish Ministerio de Ciencia e Innovación under grants AYA2008-06166-C03-03 and CSD2006-70. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. The DENIS project has been partly funded by the SCIENCE and the HCM plans of the European Commission under grants CT920791 and CT940627. It is supported by INSU, MEN and CNRS in France, by the State of Baden-Württemberg in Germany, by DGICYT in Spain, by CNR in Italy, by FFwFBWF in Austria, by FAPESP in Brazil, by OTKA grants F-4239 and F-013990 in Hungary, and by the ESO C&EE grant A-04-046. Jean Claude Renault from IAP was the Project manager. Observations were carried out thanks to the contribution of numerous students and young scientists from all involved institutes, under the supervision of P. Fouqué, survey astronomer resident in Chile. The WHT is operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. The observation presented here was taken as part of the ING service programme (proposal SW2005A06). This research has made use of Aladin, of the VizieR catalogue access tool and of Simbad at CDS, Strasbourg, France

Analyse statistique du catalogue de sources X cosmiques du satellite ESA XMM-Newton

XMM Newton est un satellite de l'ESA lancé en 1999. Il observe le ciel dans le domaine des rayons X. Le Survey Science Center (SSC) d'XMM est en charge de l'exploitation scientifique du télescope spatial et a créé à partir de ses observation le plus grandXMM Newton is a European satellite designed by ESA and launched in 1999. It scans the sky in the X-ray wavelengths. The XMM Survey Science Centre (SSC) is in charge of the scientific exploitation of the spatial telescope and has built from its observatio

Analyse statistique du catalogue de sources X cosmiques du satellite ESA XMM-Newton

XMM Newton est un satellite de l'ESA lancé en 1999. Il observe le ciel dans le domaine des rayons X. Le Survey Science Center (SSC) d'XMM est en charge de l'exploitation scientifique du télescope spatial et a créé à partir de ses observation le plus grand catalogue de sources X à ce jour. La classification des sources de ce catalogue est une des tâches attribuées au SSC. C est dans ce cadre que s'insère ce travail de thèse. Nous avons développé et implémenté un algorithme de corrélation croisée permettant d'obtenir des informations multi-longueur d'onde pour un certain nombres de sources du catalogue 2XMMi. Cet algorithme utilise une approche Bayesienne pour fournir des probabiltés d'identification. Il utilise également une approche originale permettant de se passer de simulations de Monte Carlo pour l'estimation du nombre de fausses associations. Nous avons implémenté et utilisé une analyse en composantes principale (ACP) qui prend en compte les erreurs de mesure de chaque observations. Cette ACP nous a permis d'explorer l'espace des paramètres multi-longueur d'onde et de choisir un nombre restreint de dimensions en entrée d'algorithme de classification. Nous avons utilisé les données de l'ACDS et les résultats de la corrélation entre le 2XMMi et le SDSS DR7 pour créér un échantillon d'identification. Cette échantillon est utilisé pour la création d'échantillons d'apprentissage qui sont indispensables pour la classification supervisée. Nous avons classé les sources X de plusieurs échantillons : celui issu des résultats de la corrélation 2XMMi/SDSS DR7, celui issu de la corrélation 2XMMi/GSC2/2MASS et un échantillon de sources purement X. L'algorithme de classification a été choisi après avoir comparé différentes méthodes. Enfin, nos résultats ont été insérés dans la XCAT-DB pour une large diffusion.XMM Newton is a European satellite designed by ESA and launched in 1999. It scans the sky in the X-ray wavelengths. The XMM Survey Science Centre (SSC) is in charge of the scientific exploitation of the spatial telescope and has built from its observations the larger catalogue of X-ray sources ever published so far. The work presented here has been carried out in this framework. We designed and implemented a cross-correlation algorithm that allows to retrieve multi-wavelength data for a fraction of 2XMMi catalogue sources. This algorithm is based on a Bayesian approach providing probabilities of identification. It also uses a new way to compute the rates of spurious associations without resorting to Monte Carlo simulations. We implemented and applied a principal component analysis (PCA) taking into account the measurement errors of each source. That modified PCA has been used to explore the multi-wavelength parameter space and to restrict the number of dimensions in input of the classification algorithms. ACDS data has been used together with the results of the cross-correlation of the 2XMMi with the SDSS DR7 to build an identification sample. We derived learning samples from this sample. Learning samples are essential to perform supervised classification. X-ray sources of several samples have been classified. Those samples are the 2XMMi/SDSS DR7, coming from the cross-correlation of the two catalogues, the 2XMMi/GSC2/2MASS and one only containing XMM X-ray sources. The classification algorithm used has been chosen from the results of tests performed and several methods such as decision trees, Kohonen neural networks, meanshifts or kernel density classification. Finally, we put our results into the XCAT-DB so that they are available for the rest of astronomy community

A Triplestore Implementation of the IVOA Provenance Data Model

The IVOA (International Virtual Observatory Alliance) has proposed a standard for capturing the provenance metadata in the production and distribution of astronomical data. We present an implementation in a triplestore for the provenance information recorded for a collection of astronomical images. The ontology applied is derived from PROV-O from the W3C and from the IVOA Provenance data model. SPARQL queries based on the data model concepts allow to select datasets on a wide range of provenance properties and have proven to be efficient in the triplestore representation. The data model of the SIMBAD CDS database has also been tested, and turned out to scale very efficiently in the triplestore strategy as well

Analyse statistique du catalogue de sources X cosmiques du satellite ESA XMM-Newton

XMM Newton est un satellite de l ESA lancé en 1999. Il observe le ciel dans le domaine des rayons X. Le Survey Science Center (SSC) d XMM est en charge de l exploitation scientifique du télescope spatial et a créé à partir de ses observation le plus grand catalogue de sources X à ce jour. La classification des sources de ce catalogue est une des tâches attribuées au SSC. C est dans ce cadre que s insère ce travail de thèse. Nous avons développé et implémenté un algorithme de corrélation croisée permettant d obtenir des informations multi-longueur d onde pour un certain nombres de sources du catalogue 2XMMi. Cet algorithme utilise une approche Bayesienne pour fournir des probabiltés d identification. Il utilise également une approche originale permettant de se passer de simulations de Monte Carlo pour l estimation du nombre de fausses associations. Nous avons implémenté et utilisé une analyse en composantes principale (ACP) qui prend en compte les erreurs de mesure de chaque observations. Cette ACP nous a permis d explorer l espace des paramètres multi-longueur d onde et de choisir un nombre restreint de dimensions en entrée d algorithme de classification. Nous avons utilisé les données de l ACDS et les résultats de la corrélation entre le 2XMMi et le SDSS DR7 pour créér un échantillon d identification. Cette échantillon est utilisé pour la création d échantillons d apprentissage qui sont indispensables pour la classification supervisée. Nous avons classé les sources X de plusieurs échantillons : celui issu des résultats de la corrélation 2XMMi/SDSS DR7, celui issu de la corrélation 2XMMi/GSC2/2MASS et un échantillon de sources purement X. L algorithme de classification a été choisi après avoir comparé différentes méthodes. Enfin, nos résultats ont été insérés dans la XCAT-DB pour une large diffusion.XMM Newton is a European satellite designed by ESA and launched in 1999. It scans the sky in the X-ray wavelengths. The XMM Survey Science Centre (SSC) is in charge of the scientific exploitation of the spatial telescope and has built from its observations the larger catalogue of X-ray sources ever published so far. The work presented here has been carried out in this framework. We designed and implemented a cross-correlation algorithm that allows to retrieve multi-wavelength data for a fraction of 2XMMi catalogue sources. This algorithm is based on a Bayesian approach providing probabilities of identification. It also uses a new way to compute the rates of spurious associations without resorting to Monte Carlo simulations. We implemented and applied a principal component analysis (PCA) taking into account the measurement errors of each source. That modified PCA has been used to explore the multi-wavelength parameter space and to restrict the number of dimensions in input of the classification algorithms. ACDS data has been used together with the results of the cross-correlation of the 2XMMi with the SDSS DR7 to build an identification sample. We derived learning samples from this sample. Learning samples are essential to perform supervised classification. X-ray sources of several samples have been classified. Those samples are the 2XMMi/SDSS DR7, coming from the cross-correlation of the two catalogues, the 2XMMi/GSC2/2MASS and one only containing XMM X-ray sources. The classification algorithm used has been chosen from the results of tests performed and several methods such as decision trees, Kohonen neural networks, meanshifts or kernel density classification. Finally, we put our results into the XCAT-DB so that they are available for the rest of astronomy community.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Pyrrhic victory for bark beetles: Successful standing tree colonization triggers strong intraspecific competition for offspring of Ips sexdentatus

Most bark beetles living on standing trees must overcome the natural resistance of their host to succeed in colonization. For this they perform mass attacks to reach a critical threshold of attack density (CTAD) above which host defences are exhausted. However, this strategy can result in an intense intraspecific competition during larval development. Consequently, the ability of a bark beetle species to sustain outbreaks on standing trees would be conditioned by three key factors: the value of CTAD; the ability of attacking beetles to stop accumulating after CTAD has been reached; and the ability of offspring to tolerate intraspecific competition. To test these hypotheses, we assessed attack and colonization densities of Ips sexdentatus during an outbreak, and estimated CTAD, using a stand-scale approach, in nine maritime pine stands. We also estimated the effect of intraspecific competition on the productivity and fitness of I. sexdentatus offspring, testing increasing rearing densities in the laboratory. The overall CTAD of I. sexdentatus on standing maritime pines was 142 attacks/m2. CTAD varied among stands and ranged from 53 to 177 attacks/m2. In several stands, attack densities raised much above local CTAD. Ips sexdentatus exhibited a low tolerance to intraspecific competition. The number of offspring per female and the fitness of emerging adults decreased exponentially with increasing rearing density. Excessive attack densities and negative feedback on offspring quantity and quality are likely to trigger rapid collapse of outbreaks. This supports the prediction that I. sexdentatus is an opportunistically aggressive species